Binary-scale calculations
Convert powers-of-two quantities into exponents, such as determining bit widths or binary scales.
NumPy · Function
np.log2
Computes the base-2 logarithm of each input value. It is a NumPy ufunc that supports scalar and array inputs, with standard controls for output storage, masking, casting, dtype, and subclasses.
- Since
- 1.3.0
How it works
numpy.log2 applies the base-2 logarithm element by element to its input. It accepts scalars and array-like values, supports optional output storage and masked evaluation, and returns a scalar for scalar input or an array for array input.
Syntax
numpy.log2(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True [, signature])Parameters
| Name | Type | Description |
|---|---|---|
x | array_like | Input value or array. |
out | ndarray, None, or tuple of ndarray and None | Optional output destination. |
where | array_like of bool | Boolean mask selecting where the result is computed. |
casting | {‘no’, ‘equiv’, ‘safe’, ‘same_kind’, ‘unsafe’}, optional | Casting rule for operands. |
order | {‘K’, ‘A’, ‘C’, ‘F’}, optional | Memory layout order for the result. |
dtype | dtype, optional | Output data type. |
subok | bool, optional | Whether to return subclasses of input arrays. |
signature | None or str, optional | Optional generalized ufunc signature. |
Returns
ndarray: Base-2 logarithm of x; this is a scalar if x is a scalar.
Key points
- It calculates logarithm base 2 for each input value.
- The documented call accepts the input plus standard ufunc controls such as
out,where,casting,order,dtype, andsubok. - Use
outto write computed values into an existing destination. - Real-valued inputs yield real-valued results.
- For complex results, NumPy uses the principal-value convention with imaginary parts in the interval [-π, π].
Common mistakes
- Real inputs produce real output; use complex-valued input when complex logarithms are required.
- Do not assume the result is always an ndarray: scalar input produces a scalar.
Example: Compute powers of two
import numpy as np
x = np.array([1, 2, 4, 16])
y = np.log2(x)
print(y) # [0. 1. 2. 4.]Computes the base-2 logarithm element by element.
When to use it
Logarithmic data transformation
Transform positive numeric arrays when a base-2 logarithmic scale is needed.
Complex-valued analysis
Compute complex logarithms using NumPy's documented principal-value convention.
Interview questions
QWhat does numpy.log2 compute, and what kind of operation is it?
np.log2(x) computes the base-2 logarithm of x element by element. It is a NumPy ufunc, so it works with scalars and arrays.
Follow-up: What happens for a scalar input? The result is a scalar rather than an ndarray.
QWhat values does np.log2 return for powers of two and zero?
For an array such as [0, 1, 2, 16], the results are [-inf, 0., 1., 4.]. Zero therefore produces negative infinity in the documented example.
Follow-up: How can computation be restricted to selected elements? Supply a Boolean where mask and optionally an out array.
QWhich important ufunc options does numpy.log2 expose?
The function accepts out for placing results into an existing destination and where for selecting where the ufunc is evaluated. Other ufunc controls include casting, order, dtype, and subok.
Follow-up: Why might out be useful? It can avoid allocating a separate result array when writing into a prepared destination is appropriate.
Related
numpy.lognumpy.log10numpy.log1pnumpy.emath.log2